Artificial intelligence and machine learning approaches in composting process: A review - CAĞCAĞ YOLCU, ÖZGE (My University, 2023)
© 2022 Elsevier LtdStudies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, and managing complex variables. This review investigates the perspectives and challenges of ML and its important algorithms such as Artificial Neural Networks (ANNs), Random Forest (RF), Adaptive-network-based fuzzy inference systems (ANFIS), Support Vector Machines (SVMs), and Deep Neural Networks (DNNs) used in the composting process. In addition, the individual shortcomings and inadequacies of the metrics, which were used as error or performance criteria in the studies, were emphasized. Except for a few studies, it was concluded that Artificial Intelligence (AI) algorithms such as Genetic algorithm (GA), Differential Evaluation Algorithm (DEA), and Particle Swarm Optimization (PSO) were not used in the optimization of the model parameters, but in the optimization of the parameters of the ML algorithms.
Keywords: Tarımsal Bilimler; Ziraat; Tarım Makineleri; Tarımda Enerji; Biyoyakıt Teknolojisi; Biyomedikal Mühendisliği; Mühendislik ve Teknoloji; Agricultural Sciences; Agriculture; Farm Machinery; Energy in Agriculture; Biofuels Technology; Biomedical Engineering; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Mühendislik; MÜHENDİSLİK, ÇEVRE; ENERJİ VE YAKITLAR; MÜHENDİSLİK, BİYOMEDİKAL; Engineering, Computing & Technology (ENG); ENGINEERING; ENGINEERING, ENVIRONMENTAL; ENERGY & FUELS; ENGINEERING, BIOMEDICAL; Biyomühendislik; Fizik Bilimleri; Çevre Mühendisliği; Yenilenebilir Enerji, Sürdürülebilirlik ve Çevre; Atık Yönetimi ve Bertarafı; Bioengineering; Physical Sciences; Environmental Engineering; Renewable Energy, Sustainability and the Environment; Waste Management and Disposal; Composting; Machine learning; Maturity; Modeling; Process stability